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1. On the extraction of RF fingerprints from LSTM hidden-state values for robust open-set detection

   https://doi.org/10.52953/MOGL1293  

   Puppo, Luke; Wong, Weng-Keen; Hamdaoui, Bechir; Elmaghbub, Abdurrahman; Lin, Lucy (January 2024, ITU Journal on Future and Evolving Technologies)

   New capabilities in wireless network security have been enabled by deep learning that leverages and exploits signal patterns and characteristics in Radio Frequency (RF) data captured by radio receivers to identify and authenticate radio transmitters. Open-set detection is an area of deep learning that aims to identify RF data samples captured from new devices during deployment (aka inference) that were not part of the training set; i.e. devices that were unseen during training. Past work in open-set detection has mostly been applied to independent and identically distributed data such as images. In contrast, RF signal data present a unique set of challenges as the data forms a time series with non-linear time dependencies among the samples. In this paper, we introduce a novel open-set detection approach for RF data-driven device identification that extracts its neural network features from patterns of the hidden state values within a Convolutional Neural Network Long Short-Term Memory (CNN+LSTM) model. Experimental results obtained using real datasets collected from 15 IoT devices, each enabled with LoRa, wireless-Wi-Fi, and wired-Wi-Fi communication protocols, show that our new approach greatly improves the area under the precision-recall curve, and hence, can be used successfully to monitor and control unauthorized network access of wireless devices. 

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2. On Modeling and Optimizing LTE/Wi-Fi Coexistence with Prioritized Traffic Classes

   https://doi.org/10.1109/DySPAN.2018.8610465  

   Hirzallah, Mohammed; Xiao, Yong; Krunz, Marwan (October 2018, 2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN))

   The dramatic growth in demand for mobile data service has prompted mobile network operators (MNOs) to explore new spectrum resources in unlicensed bands. MNOs have been recently allowed to extend LTE-based service called LTE-LAA over 5 GHz U-NII bands, currently occupied by Wi-Fi. To support applications with diverse QoS requirements, both LTE and Wi-Fi technologies introduce multiple priority classes with different channel contention parameters for accessing unlicensed bands. How these different priority classes affect the interplay between coexisting LTE and Wi-Fi technologies is still relatively under-explored. In this paper, we develop a simple and efficient framework that helps MNOs assess the fair coexistence between MNOs and Wi-Fi operators with prioritized channel access under the multi-channel setting. We derive an approximated closed-form solution for each MNO to pre-evaluate the probability of successful transmission (PST), average contention delay, and average throughput when adopting different priority classes to serve different traffics. MNOs and Wi-Fi operators can fit our model using measurements collected offline and/or online, and use it to further optimize their systems’ throughput and latency. Our results reveal that PSTs computed with our approximated closed-form model approach those collected from system-level simulations with around 95% accuracy under scenarios of dense network deployment density and high traffic intensity. 

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3. Detection Through Deep Neural Networks: A Reservoir Computing Approach for MIMO-OFDM Symbol Detection

   Kangjun Bai, Lingjia Liu (November 2020, IEEE International Conference on Computer-Aided Design)

   null (Ed.)

   The Reservoir Computing, a neural computing framework suited for temporal information processing, utilizes a dynamic reservoir layer for high-dimensional encoding, enhancing the separability of the network. In this paper, we exploit a Deep Learning (DL)-based detection strategy for Multiple-input, Multiple-output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) symbol detection. To be specific, we introduce a Deep Echo State Network (DESN), a unique hierarchical processing structure with multiple time intervals, to enhance the memory capacity and accelerate the detection efficiency. The resulting hardware prototype with the hybrid memristor-CMOS co-design provides in-memory computing and parallel processing capabilities, significantly reducing the hardware and power overhead. With the standard 180nm CMOS process and memristive synapses, the introduced DESN consumes merely 105mW of power consumption, exhibiting 16.7% power reduction compared to shallow ESN designs even with more dynamic layers and associated neurons. Furthermore, numerical evaluations demonstrate the advantages of the DESN over state-of-the-art detection techniques in the literate for MIMO-OFDM systems even with a very limited training set, yielding a 47.8% improvement against conventional symbol detection techniques. 

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4. Turbo-Connected Neural Network Media Noise Cancellation Strategy for Asynchronous Multitrack Detection

   https://doi.org/10.1109/TMRC59626.2023.10264010  

   Banan_Sadeghian, Elnaz (October 2023, 2023 IEEE 34th Magnetic Recording Conference (TMRC))

   Multitrack detection architectures provide throughput and areal density gains over the current industry’s standard of single-track detection architectures. One major challenge of multitrack architectures is the complexity of implementing conventional pattern-dependent media noise prediction (PDNP) strategy within the multitrack symbol detector. In this paper we propose a neural network media noise predictor with manageable complexity that iterates with our rotating target (ROTAR) symbol detector in the turbo equalization fashion to predict and cancel the media noise for multitrack detection of asynchronous tracks. We evaluate the proposed detection strategy on a realistic two-dimensional magnetic-recording channel, and find that the proposed solution can effectively mitigate the media noise and therefore can replace the prohibitively complex PDNP solution for multitrack detection. 

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5. Spatio-Temporal Graph Attention Embedding for Joint Crowd Flow and Transition Predictions: A Wi-Fi-based Mobility Case Study

   https://doi.org/10.1145/3495003  

   Yang, Xi; He, Suining; Wang, Bing; Tabatabaie, Mahan (December 2021, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Crowd mobility prediction, in particular, forecasting flows at and transitions across different locations, is essential for crowd analytics and management in spacious environments featured with large gathering. We propose GAEFT, a novel crowd mobility analytics system based on the multi-task graph attention neural network to forecast crowd flows (inflows/outflows) and transitions. Specifically, we leverage the collective and sanitized campus Wi-Fi association data provided by our university information technology service and conduct a relatable case study. Our comprehensive data analysis reveals the important challenges of sparsity and skewness, as well as the complex spatio-temporal variations within the crowd mobility data. Therefore, we design a novel spatio-temporal clustering method to group Wi-Fi access points (APs) with similar transition features, and obtain more regular mobility features for model inputs. We then propose an attention-based graph embedding design to capture the correlations among the crowd flows and transitions, and jointly predict the AP-level flows as well as transitions across buildings and clusters through a multi-task formulation. Extensive experimental studies using more than 28 million association records collected during 2020-2021 academic year validate the excellent accuracy of GAEFT in forecasting dynamic and complex crowd mobility. 

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